Profiling is achieved by instrumenting either the program source code or its binary executable form using a tool called a profiler (or code profiler). Profilers may use a number of different techniques, such as event-based, statistical, instrumented, and simulation methods.

Program analysis tools are extremely important for understanding program behavior. Computer architects need such tools to evaluate how well programs will perform on new architectures. Software writers need tools to analyze their programs and identify critical sections of code. Compiler writers often use such tools to find out how well their instruction scheduling or branch prediction algorithm is performing...

For sequential programs, a summary profile is usually sufficient, but performance problems in parallel programs (waiting for messages or synchronization issues) often depend on the time relationship of events, thus requiring a full trace to get an understanding of what is happening.

The size of a (full) trace is linear to the program's instruction path length, making it somewhat impractical. A trace may therefore be initiated at one point in a program and terminated at another point to limit the output.

An ongoing interaction with the hypervisor (continuous or periodic monitoring via on-screen display for instance)

This provides the opportunity to switch a trace on or off at any desired point during execution in addition to viewing on-going metrics about the (still executing) program. It also provides the opportunity to suspend asynchronous processes at critical points to examine interactions with other parallel processes in more detail.

A profiler can be applied to an individual method or at the scale of a module or program, to identify performance bottlenecks by making long-running code obvious.[1] A profiler can be used to understand code from a timing point of view, with the objective of optimizing it to handle various runtime conditions[2] or various loads[3]. Profiling results can be ingested by a compiler that provides profile-guided optimization.[4] Profiling results can be used to guide the design and optimization of an individual algorithm; the Krauss matching wildcards algorithm is an example.[5] Profilers are built into some application performance management systems that aggregate profiling data to provide insight into transaction workloads in distributed applications.[6]

Profiler-driven program analysis on Unix dates back to 1973 [7], when Unix systems included a basic tool, prof, which listed each function and how much of program execution time it used. In 1982 gprof extended the concept to a complete call graph analysis.[8]

In 1994, Amitabh Srivastava and Alan Eustace of Digital Equipment Corporation published a paper describing ATOM[9](Analysis Tools with OM). The ATOM platform converts a program into its own profiler: at compile time, it inserts code into the program to be analyzed. That inserted code outputs analysis data. This technique - modifying a program to analyze itself - is known as "instrumentation".

In 2004 both the gprof and ATOM papers appeared on the list of the 50 most influential PLDI papers for the 20-year period ending in 1999.[10]

Input-sensitive profilers[11][12][13] add a further dimension to flat or call-graph profilers by relating performance measures to features of the input workloads, such as input size or input values. They generate charts that characterize how an application's performance scales as a function of its input.

Profilers, which are also programs themselves, analyze target programs by collecting information on their execution. Based on their data granularity, on how profilers collect information, they are classified into event based or statistical profilers. Profilers interrupt program execution to collect information, which may result in a limited resolution in the time measurements, which should be taken with a grain of salt. Basic block profilers report a number of machine clock cycles devoted to executing each line of code, or a timing based on adding these together; the timings reported per basic block may not reflect a difference between cache hits and misses.[14][15]

.NET: Can attach a profiling agent as a COM server to the CLR using Profiling API. Like Java, the runtime then provides various callbacks into the agent, for trapping events like method JIT / enter / leave, object creation, etc. Particularly powerful in that the profiling agent can rewrite the target application's bytecode in arbitrary ways.

Python: Python profiling includes the profile module, hotshot (which is call-graph based), and using the 'sys.setprofile' function to trap events like c_{call,return,exception}, python_{call,return,exception}.

Ruby: Ruby also uses a similar interface to Python for profiling. Flat-profiler in profile.rb, module, and ruby-prof a C-extension are present.

Some profilers operate by sampling. A sampling profiler probes the target program's call stack at regular intervals using operating systeminterrupts. Sampling profiles are typically less numerically accurate and specific, but allow the target program to run at near full speed.

The resulting data are not exact, but a statistical approximation. "The actual amount of error is usually more than one sampling period. In fact, if a value is n times the sampling period, the expected error in it is the square-root of n sampling periods." [16]

In practice, sampling profilers can often provide a more accurate picture of the target program's execution than other approaches, as they are not as intrusive to the target program, and thus don't have as many side effects (such as on memory caches or instruction decoding pipelines). Also since they don't affect the execution speed as much, they can detect issues that would otherwise be hidden. They are also relatively immune to over-evaluating the cost of small, frequently called routines or 'tight' loops. They can show the relative amount of time spent in user mode versus interruptible kernel mode such as system call processing.

Still, kernel code to handle the interrupts entails a minor loss of CPU cycles, diverted cache usage, and is unable to distinguish the various tasks occurring in uninterruptible kernel code (microsecond-range activity).

Dedicated hardware can go beyond this: ARM Cortex-M3 and some recent MIPS processors JTAG interface have a PCSAMPLE register, which samples the program counter in a truly undetectable manner, allowing non-intrusive collection of a flat profile.

This technique effectively adds instructions to the target program to collect the required information. Note that instrumenting a program can cause performance changes, and may in some cases lead to inaccurate results and/or heisenbugs. The effect will depend on what information is being collected, on the level of timing details reported, and on whether basic block profiling is used in conjunction with instrumentation.[23] For example, adding code to count every procedure/routine call will probably have less effect than counting how many times each statement is obeyed. A few computers have special hardware to collect information; in this case the impact on the program is minimal.

Instrumentation is key to determining the level of control and amount of time resolution available to the profilers.

Manual: Performed by the programmer, e.g. by adding instructions to explicitly calculate runtimes, simply count events or calls to measurement APIs such as the Application Response Measurement standard.

Automatic source level: instrumentation added to the source code by an automatic tool according to an instrumentation policy.

Interpreter debug options can enable the collection of performance metrics as the interpreter encounters each target statement. A bytecode, control table or JIT interpreters are three examples that usually have complete control over execution of the target code, thus enabling extremely comprehensive data collection opportunities.

1.
IBM System/360
–
The IBM System/360 is a mainframe computer system family that was announced by IBM on April 7,1964, and delivered between 1965 and 1978. It was the first family of computers designed to cover the range of applications, from small to large. The design made a distinction between architecture and implementation, allowing IBM to release a suite of compatible designs at different prices. The launch of the System/360 family introduced IBMs Solid Logic Technology, the slowest System/360 model announced in 1964, the Model 30, could perform up to 34,500 instructions per second, with memory from 8 to 64 KB. The 1967 IBM System/360 Model 91 could do up to 16.6 million instructions per second, up to 8 megabytes of slower Large Capacity Storage was also available. Many consider the one of the most successful computers in history. The chief architect of System/360 was Gene Amdahl, and the project was managed by Fred Brooks, the commercial release was piloted by another of Watsons lieutenants, John R. Opel, who managed the launch of IBM’s System 360 mainframe family in 1964. Application level compatibility for System/360 software is maintained to present day with the System z servers, contrasting with at-the-time normal industry practice, IBM created an entire series of computers, from small to large, low to high performance, all using the same instruction set. This feat allowed customers to use a model and then upgrade to larger systems as their needs increased without the time. This flexibility greatly lowered barriers to entry, with other vendors, customers had to choose between machines they could outgrow and machines that were potentially overpowered. This meant that many simply did not buy computers. IBM initially announced a series of six computers and forty common peripherals, IBM eventually delivered fourteen models, including rare one-off models for NASA. The initial announcement in 1964 included Models 30,40,50,60,62, the first three were low- to middle-range systems aimed at the IBM1400 series market. All three first shipped in mid-1965, the last three, intended to replace the 7000 series machines, never shipped and were replaced by the 65 and 75, which were first delivered in November 1965, and January 1966, respectively. Later additions to the low-end included models 20,22, and 25, the Model 20 had several sub-models, sub-model 5 was at the higher end of the model. A succession of high-end machines included the Model 67,85,91,95, the 85 design was intermediate between the System/360 line and the follow-on System/370 and was the basis for the 370/165. There was a System/370 version of the 195, but it did not include Dynamic Address Translation, the implementations differed substantially, using different native data path widths, presence or absence of microcode, yet were extremely compatible. Except where specifically documented, the models were architecturally compatible, the 91, for example, was designed for scientific computing and provided out-of-order instruction execution, but lacked the decimal instruction set used in commercial applications

2.
Node.js
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Node. js is an open-source, cross-platform JavaScript runtime environment for developing a diverse variety of server tools and applications. Although Node. js is not a JavaScript framework, many of its modules are written in JavaScript. The runtime environment interprets JavaScript using Googles V8 JavaScript engine, Node. js has an event-driven architecture capable of asynchronous I/O. These design choices aim to optimize throughput and scalability in Web applications with many input/output operations, the Node. js distributed development project, governed by the Node. js Foundation, is facilitated by the Linux Foundations Collaborative Projects program. Corporate users of Node. js software include GoDaddy, Groupon, IBM, LinkedIn, Microsoft, Netflix, PayPal, Rakuten, SAP, Voxer, Walmart, Yahoo. Node. js was originally written in 2009 by Ryan Dahl. The initial release supported only Linux and Mac OSX and its development and maintenance was led by Dahl and later sponsored by Joyent. Dahl was inspired to create Node. js after seeing a file upload progress bar on Flickr, the browser did not know how much of the file had been uploaded and had to query the Web server. Dahl demonstrated the project at the inaugural European JSConf on November 8,2009, Node. js combined Googles V8 JavaScript engine, an event loop and a low-level I/O API. The project received a standing ovation, in January 2010, a package manager was introduced for the Node. js environment called npm. The package manager makes it easier for programmers to publish and share code of Node. js libraries and is designed to simplify installation, updating. In June 2011, Microsoft and Joyent implemented a native Windows version of Node. js, the first Node. js build supporting Windows was released in July 2011. In January 2012, Dahl stepped aside, promoting coworker and npm creator Isaac Schlueter to manage the project, in January 2014, Schlueter announced that Timothy J. Fontaine would lead the project. In December 2014, Fedor Indutny started io. js, a fork of Node. js, due to the internal conflict over Joyents governance, io. js was created as an open governance alternative with a separate technical committee. Unlike Node. js, the authors planned to keep io. js up-to-date with the latest releases of the Google V8 JavaScript engine, in February 2015, the intent to form a neutral Node. js Foundation was announced. By June 2015, the Node. js and io. js communities voted to work together under the Node. js Foundation, in September 2015, Node. js v0.12 and io. js v3.3 were merged back together into Node v4.0. This brought V8 ES6 features into Node. js, and a long-term support release cycle, as of 2016, the io. js website recommends that developers switch back to Node. js and that no further releases of io. js are planned due to the merger. Node. js allows the creation of Web servers and networking tools using JavaScript, modules are provided for file system I/O, networking, binary data, cryptography functions, data streams and other core functions. Node. jss modules use an API designed to reduce the complexity of writing server applications, Node. js applications can run on Linux, macOS, Microsoft Windows, NonStop, and Unix servers

3.
Machine code
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Machine code or machine language is a set of instructions executed directly by a computers central processing unit. Each instruction performs a specific task, such as a load. Every program directly executed by a CPU is made up of a series of such instructions, numerical machine code may be regarded as the lowest-level representation of a compiled or assembled computer program or as a primitive and hardware-dependent programming language. While it is possible to write directly in numerical machine code, it is tedious and error prone to manage individual bits and calculate numerical addresses. For this reason machine code is almost never used to programs in modern contexts. Almost all practical programs today are written in languages or assembly language. However, the interpreter itself, which may be seen as an executor or processor, performing the instructions of the source code, every processor or processor family has its own machine code instruction set. Instructions are patterns of bits that by physical design correspond to different commands to the machine, thus, the instruction set is specific to a class of processors using the same architecture. Successor or derivative processor designs often include all the instructions of a predecessor, systems may also differ in other details, such as memory arrangement, operating systems, or peripheral devices. Because a program normally relies on such factors, different systems will not run the same machine code. A machine code instruction set may have all instructions of the same length, how the patterns are organized varies strongly with the particular architecture and often also with the type of instruction. Not all machines or individual instructions have explicit operands, an accumulator machine has a combined left operand and result in an implicit accumulator for most arithmetic instructions. Other architectures have accumulator versions of common instructions, with the accumulator regarded as one of the general registers by longer instructions, a stack machine has most or all of its operands on an implicit stack. Special purpose instructions also often lack explicit operands and this distinction between explicit and implicit operands is important in machine code generators, especially in the register allocation and live range tracking parts. A good code optimizer can track implicit as well as explicit operands which may allow more frequent constant propagation, constant folding of registers, a computer program is a sequence of instructions that are executed by a CPU. While simple processors execute instructions one after another, superscalar processors are capable of executing several instructions at once, program flow may be influenced by special jump instructions that transfer execution to an instruction other than the numerically following one. Conditional jumps are taken or not depending on some condition, a much more readable rendition of machine language, called assembly language, uses mnemonic codes to refer to machine code instructions, rather than using the instructions numeric values directly. For example, on the Zilog Z80 processor, the machine code 00000101, the MIPS instruction set provides a specific example for a machine code whose instructions are always 32 bits long

4.
Microcode
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Microcode is a technique that imposes an interpreter between the hardware and the architectural level of a computer. As such, the microcode is a layer of hardware-level instructions that implement higher-level machine code instructions or internal state machine sequencing in many digital processing elements. Microcode typically resides in special high-speed memory and translates machine instructions and it separates the machine instructions from the underlying electronics so that instructions can be designed and altered more freely. It also facilitates the building of complex multi-step instructions, while reducing the complexity of computer circuits, writing microcode is often called microprogramming and the microcode in a particular processor implementation is sometimes called a microprogram. Some hardware vendors, especially IBM, use the term microcode as a synonym for firmware, when compared to normal application programs, the elements composing a microprogram exist on a lower conceptual level. To avoid confusion, each microprogram-related element is differentiated by the prefix, microinstruction, microassembler, microprogrammer, microarchitecture. Engineers normally write the microcode during the phase of a processor, storing it in a read-only memory or programmable logic array structure. However, machines also exist that have some or all microcode stored in SRAM or flash memory and this is traditionally denoted as writeable control store in the context of computers, which can be either read-only or read-write memory. Complex digital processors may also more than one control unit in order to delegate sub-tasks that must be performed essentially asynchronously in parallel. A high-level programmer, or even an assembly programmer, does not normally see or change microcode, microprograms consist of series of microinstructions, which control the CPU at a very fundamental level of hardware circuitry. g. 128 bits on a 360/85 with an emulator feature, microcode was originally developed as a simpler method of developing the control logic for a computer. Initially, CPU instruction sets were hardwired, each step needed to fetch, decode, and execute the machine instructions was controlled directly by combinational logic and rather minimal sequential state machine circuitry. Microcode simplified the job by allowing much of the processors behaviour, even late in the design process, microcode could easily be changed, whereas hard-wired CPU designs were very cumbersome to change. Thus, this greatly facilitated CPU design, the IBM Future Systems project and Data General Fountainhead Processor are examples of this. During the 1970s, CPU speeds grew more quickly than memory speeds and numerous techniques such as memory block transfer, memory pre-fetch, high-level machine instructions, made possible by microcode, helped further, as fewer more complex machine instructions require less memory bandwidth. For example, an operation on a string can be done as a single machine instruction. Architectures with instruction sets implemented by complex microprograms included the IBM System/360, the approach of increasingly complex microcode-implemented instruction sets was later called CISC. An alternate approach, used in microprocessors, is to use PLAs or ROMs mainly for instruction decoding

5.
Software engineering
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Software engineering is the application of engineering to the development of software in a systematic method. Practitioners quickly realized that this design was not flexible and came up with the stored program architecture or von Neumann architecture, thus the division between hardware and software began with abstraction being used to deal with the complexity of computing. Programming languages started to appear in the early 1950s and this was another major step in abstraction. Major languages such as Fortran, ALGOL, and COBOL were released in the late 1950s to deal with scientific, algorithmic, the conference was attended by international experts on software who agreed on defining best practices for software grounded in the application of engineering. The result of the conference is a report that defines how software should be developed, the original report is publicly available. Engineering already addresses all issues, hence the same principles used in engineering can be applied to software. The widespread lack of best practices for software at the time was perceived as a software crisis, barry W. Boehm documented several key advances to the field in his 1981 book, Software Engineering Economics. These include his Constructive Cost Model, which relates software development effort for a program, in man-years T, T = k ∗ The book analyzes sixty-three software projects and concludes the cost of fixing errors escalates as the project moves toward field use. The book also asserts that the key driver of software cost is the capability of the development team. Watts Humphrey founded the SEI Software Process Program, aimed at understanding and managing the software engineering process and his 1989 book, Managing the Software Process, asserts that the Software Development Process can and should be controlled, measured, and improved. Modern, generally accepted best-practices for software engineering have been collected by the ISO/IEC JTC 1/SC7 subcommittee, Software engineering can be divided into 15 sub-disciplines. They are, Software requirements, The elicitation, analysis, specification, Software design, The process of defining the architecture, components, interfaces, and other characteristics of a system or component. It is also defined as the result of that process, Software construction, The detailed creation of working, meaningful software through a combination of coding, verification, unit testing, integration testing, and debugging. Software testing, An empirical, technical investigation conducted to provide stakeholders with information about the quality of the product or service under test, Software maintenance, The totality of activities required to provide cost-effective support to software. Software development process, The definition, implementation, assessment, measurement, management, change, many software engineers enter the profession by obtaining a university degree or training at a vocational school. One standard international curriculum for undergraduate software engineering degrees was defined by the CCSE, in addition to university education, many companies sponsor internships for students wishing to pursue careers in information technology. These internships can introduce the student to interesting real-world tasks that typical software engineers encounter every day, similar experience can be gained through military service in software engineering. Legal requirements for the licensing or certification of professional software engineers vary around the World, in Canada, there is a legal requirement to have P. Eng when one wants to use the title engineer or practice software engineering

6.
Application performance management
–
In the fields of information technology and systems management, application performance management is the monitoring and management of performance and availability of software applications. APM strives to detect and diagnose complex application performance problems to maintain a level of service. APM is the translation of IT metrics into business meaning, two sets of performance metrics are closely monitored. The first set of performance metrics defines the performance experienced by end users of the application, one example of performance is average response times under peak load. The components of the set include load and response times, the load is the volume of transactions processed by the application, e. g. transactions per second, requests per second, pages per second. The response times are the times required for an application to respond to a users actions at such a load, measurement of these quantities establishes an empirical performance baseline for the application. The baseline can then be used to detect changes in performance, changes in performance can be correlated with external events and subsequently used to predict future changes in application performance. The use of APM is common for Web applications, which lends itself best to the more detailed monitoring techniques, in addition to measuring response time for a user, response times for components of a Web application can also be monitored to help pinpoint causes of delay. There also exist HTTP appliances that can decode transaction-specific response times at the Web server layer of the application, since the first half of 2013, APM has entered into a period of intense competition of technology and strategy with a multiplicity of vendors and viewpoints. This has caused an upheaval in the marketplace with vendors from unrelated backgrounds adopting messaging around APM, as a result, the term APM has become diluted and has evolved into a concept for managing application performance across many diverse computing platforms, rather than a single market. To alleviate the first problem application service management provides an application-centric approach, each function is now likely to have been designed as an Internet service that runs on multiple virtualized systems. The applications themselves are likely to be moving from one system to another to meet service-level objectives. The APM Conceptual Framework was designed to help prioritize an approach on what to focus on first for a quick implementation, the framework slide outlines three areas of focus for each dimension and describes their potential benefits. These areas are referenced as Primary below, with the lower priority dimensions referenced as Secondary, measuring the transit of traffic from user request to data and back again is part of capturing the end-user-experience. The outcome of this measuring is referred to as Real-time Application monitoring, passive monitoring is usually an agentless appliance implemented using network port mirroring. A key feature to consider in this solution is the ability to support multiple protocol analytics since most companies have more than just web-based applications to support, active monitoring, on the other hand, consists of synthetic probes and web robots predefined to report system availability and business transactions. Active monitoring is a complement to passive monitoring, together. User experience management is a subcategory that emerged from the EUE dimension to monitor the behavioral context of the user, UEM, as practiced today, goes beyond availability to capture latencies and inconsistencies as human beings interact with applications and other services

7.
IBM System/370
–
The IBM System/370 was a model range of IBM mainframe computers announced on June 30,1970 as the successors to the System/360 family. A Dynamic Address Translation option was not announced until 1972, 128-bit floating point arithmetic on all models, the original System/370 line underwent several architectural improvements during its roughly 20-year lifetime. The first System/370 machines, the Model 155, the Model 165, and these changes included,13 new instructions, among which were MOVE LONG, COMPARE LOGICAL LONG, thereby permitting operations on up to 2^24-1 bytes, vs. They did not include support for virtual memory, in 1972, a very significant change was made when support for virtual memory was introduced with IBMs System/370 Advanced Function announcement. IBM had initially chosen to exclude virtual storage from the S/370 line, the S/370-145 had an associative memory used by the microcode for the DOS compatibility feature from its first shipments in June 1971, the same hardware was used by the microcode for DAT. The 145 microcode architecture simplified the addition of virtual memory, allowing this capability to be present in early 145s without the extensive modifications needed in other models. The Reference and Change bits of the Storage-protection Keys, however, were labeled on the rollers, existing S/370-145 customers were happy to learn that they did not have to purchase a hardware upgrade in order to run DOS/VS or OS/VS1. After installation, these models were known as the S/370-155-II and S/370-165-II, IBM wanted customers to upgrade their 155 and 165 systems to the widely sold S/370-158 and -168. This led to the original S/370-155 and S/370-165 models being described as boat anchors, later architectural changes primarily involved expansions in memory – both physical memory and virtual address space – to enable larger workloads and meet client demands for more storage. This was the trend as Moores Law eroded the unit cost of memory. As with all IBM mainframe development, preserving backward compatibility was paramount, in October 1981, the 3033 and 3081 processors added extended real addressing, which allowed 26-bit addressing for physical storage. This capability appeared later on other systems, such as the 4381 and 3090, the cross-memory services capability which facilitated movement of data between address spaces was actually available just prior to S/370-XA architecture on the 3031,3032 and 3033 processors. As described above, the S/370 product line underwent a major architectural change, the evolution of S/370 addressing was always complicated by the basic S/360 instruction set design, and its large installed code base, which relied on a 24-bit logical address. Most shops thus continued to run their 24-bit applications in a higher-performance 31-bit world and this evolutionary implementation had the characteristic of solving the most urgent problems first, relief for real memory addressing being needed sooner than virtual memory addressing. IBMs choice of 31-bit addressing for 370-XA involved various factors, the System/360 Model 67 had included a full 32-bit addressing mode, but this feature was not carried forward to the System/370 series, which began with only 24-bit addressing. When IBM later expanded the S/370 address space in S/370-XA, several reasons are cited for the choice of 31 bits, in particular, the standard subroutine calling convention marked the final parameter word by setting its high bit. Interaction between 32-bit addresses and two instructions that treated their arguments as signed numbers, input from key initial Model 67 sites, which had debated the alternatives during the initial system design period, and had recommended 31 bits. The following table summarizes the major S/370 series and models, the second column lists the principal architecture associated with each series

8.
Computational complexity theory
–
A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying the amount of resources needed to solve them, such as time and storage. Other complexity measures are used, such as the amount of communication, the number of gates in a circuit. One of the roles of computational complexity theory is to determine the limits on what computers can. Closely related fields in computer science are analysis of algorithms. More precisely, computational complexity theory tries to classify problems that can or cannot be solved with appropriately restricted resources, a computational problem can be viewed as an infinite collection of instances together with a solution for every instance. The input string for a problem is referred to as a problem instance. In computational complexity theory, a problem refers to the question to be solved. In contrast, an instance of this problem is a rather concrete utterance, for example, consider the problem of primality testing. The instance is a number and the solution is yes if the number is prime, stated another way, the instance is a particular input to the problem, and the solution is the output corresponding to the given input. For this reason, complexity theory addresses computational problems and not particular problem instances, when considering computational problems, a problem instance is a string over an alphabet. Usually, the alphabet is taken to be the binary alphabet, as in a real-world computer, mathematical objects other than bitstrings must be suitably encoded. For example, integers can be represented in binary notation, and graphs can be encoded directly via their adjacency matrices and this can be achieved by ensuring that different representations can be transformed into each other efficiently. Decision problems are one of the objects of study in computational complexity theory. A decision problem is a type of computational problem whose answer is either yes or no. A decision problem can be viewed as a language, where the members of the language are instances whose output is yes. The objective is to decide, with the aid of an algorithm, if the algorithm deciding this problem returns the answer yes, the algorithm is said to accept the input string, otherwise it is said to reject the input. An example of a problem is the following

9.
Executable
–
These instructions are traditionally machine code instructions for a physical CPU. Executable code is used to describe sequences of instructions that do not necessarily constitute an executable file, for example. Several object files are linked to create the executable, object files, executable or not, are typically in a container format, such as Executable and Linkable Format. This structures the generated code, for example dividing it into sections such as the. text. data. In order to be executed by the system, a file must conform to the systems Application Binary Interface. For example, in ELF, the point is specified in the header in the e_entry field. In the GCC this field is set by the based on the _start symbol. For C, this is done by linking in the crt0 object, Executable files thus normally contain significant additional machine code beyond that directly generated from the specific source code. In some cases it is desirable to omit this, for example for embedded systems development or simply to understand how compilation, linking, comparison of executable file formats EXE File Format at What Is

10.
Android Runtime
–
Android Runtime is an application runtime environment used by the Android operating system. While Dalvik interprets the rest of applications bytecode, native execution of those short bytecode segments, unlike Dalvik, ART introduces the use of ahead-of-time compilation by compiling entire applications into native machine code upon their installation. As a downside, ART requires additional time for the compilation when an application is installed, Android 4.4 KitKat brought a technology preview of ART, including it as an alternative runtime environment and keeping Dalvik as the default virtual machine. In the subsequent major Android release, Android 5.0 Lollipop, Android 7.0 Nougat introduced JIT compiler with code profiling to ART, which lets it constantly improve the performance of Android apps as they run

In computing, source code is any collection of computer instructions, possibly with comments, written using a …

A more complex Java source code example. Written in object-oriented programming style, it demonstrates boilerplate code. With prologue comments indicated in red, inline comments indicated in green, and program statements indicated in blue.